p2: calculate impulse func estimate

* add visualisation code
* add pic

p2/data.go

@@ -85,7 +85,8 @@ func saveStuff(
 	varianceU, varianceY,
 	cov float64,
 	autocorrelationU, autocorrelationY,
-	mutCorrelationUY, mutCorrelationYU []float64,
+	mutCorrelationUY, mutCorrelationYU,
+	impulseFunc []float64,
 ) error {
 	fFnames := map[string]float64{
 		"mean_u":        meanU,
@@ -99,6 +100,7 @@ func saveStuff(
 		"autocorrelation_y":     autocorrelationY,
 		"mutual_correlation_uy": mutCorrelationUY,
 		"mutual_correlation_yu": mutCorrelationYU,
+		"impulse_func":          impulseFunc,
 	}
 	prefix := "data/"
 	suffix := ".txt"

p2/data/impulse_func.csv

@@ -0,0 +1,26 @@
+0.0184539137157985894122091963254206348210573196411132812500000000
+0.0473523910649251283944138890547037590295076370239257812500000000
+0.0439798428385898415582389020528353285044431686401367187500000000
+0.0370162153890833331426613028725114418193697929382324218750000000
+0.0268203707478212004244877419978365651331841945648193359375000000
+0.0293922808417747399856168044607329647988080978393554687500000000
+0.0287505536202991573979659278847975656390190124511718750000000000
+0.0251912419712964144591360593494755448773503303527832031250000000
+0.0313842198158401086693913839553715661168098449707031250000000000
+0.0288515259300295735800379048896502354182302951812744140625000000
+0.0319007668772124386435962151153944432735443115234375000000000000
+0.0306864411068723079434317213554095360450446605682373046875000000
+0.0242963375206136783512889110170362982898950576782226562500000000
+0.0194871671066619012169240932053071446716785430908203125000000000
+0.0125898199468795837785428659572062315419316291809082031250000000
+0.0215156962986396832482327567959146108478307723999023437500000000
+0.0186423123910237388545230174941025325097143650054931640625000000
+0.0213207501556608898518607730920848553068935871124267578125000000
+0.0263843382153661834554903009575355099514126777648925781250000000
+0.0308940041687151834004509964870521798729896545410156250000000000
+0.0420472567660505752717980954002996440976858139038085937500000000
+0.0546400556881618607030226542065065586939454078674316406250000000
+0.0617199329194972612366854036736185662448406219482421875000000000
+0.0421812613093787772133858027245878474786877632141113281250000000
+0.0392001223855573538434882152614591177552938461303710937500000000
+0.0607583542008108823395673425693530589342117309570312500000000000

p2/run.go

@@ -42,6 +42,11 @@ func run() error {
 
 		cov := stats.Covariance(data[u], data[y])
 
+		impulseFunc, err := stats.ImpulseFunction(autocorrelationU, mutCorrelationUY)
+		if err != nil {
+			return err
+		}
+
 		log.Printf("len(data): %d", len(data[u]))
 
 		log.Printf("means - u: %v, y: %v", meanU, meanY)
@@ -55,12 +60,16 @@ func run() error {
 		log.Printf("mutual correlation Y,U: %v", mutCorrelationYU)
 		log.Printf("covariance U,Y: %v", cov)
 
+		log.Printf("len(impulseFunc): %d", len(impulseFunc))
+		log.Printf("impulseFunc: %v", impulseFunc)
+
 		err = saveStuff(
 			meanU, meanY,
 			varianceU, varianceY,
 			cov,
 			autocorrelationU, autocorrelationY,
 			mutCorrelationUY, mutCorrelationYU,
+			impulseFunc,
 		)
 		if err != nil {
 			return err

p2/visualise.py

@@ -41,3 +41,15 @@ fig = lp.get_figure()
 fig.subplots_adjust(top=.97)
 fig.savefig('res/signal_y.png')
 plt.clf()
+
+imf=pd.read_csv('data/impulse_func.csv')
+lp = sb.lineplot(data=imf, legend=False, linewidth=2.5)
+lp.set(
+		title='Impulse Function Estimate',
+		xlabel='time (s)',
+		ylabel='y',
+	)
+fig = lp.get_figure()
+fig.subplots_adjust(top=.97)
+fig.savefig('res/impulse_func_estimate.png')
+plt.clf()